The present disclosure is directed toward a method and system for tracking a feature moving across a series of temporal related image frames and, more specifically, for generating and dynamically applying multiple templates at different scales for updating a location of the feature in each frame. The present disclosure finds application in traffic surveillance. However, it is appreciated that the present exemplary embodiments are also amendable to other like applications.
Significant strides are being made toward a development and use of video-based traffic monitoring and enforcement systems. These systems are programmed to support a variety of tasks. Mainly, the systems are implemented to manage traffic flow and to enforce vehicle compliance with traffic laws, rules, and regulations.
A method 10 performed by a conventional system is illustrated in FIG. 1. A camera captures a video stream of a monitored region at S12. The camera may transmit video data to a remote server device for further processing starting at S14 or perform some or all of the processing on-board. This processing can be based on a function of the system. Generally, the processing can define a target area for detecting a vehicle. Using the processed information, the method 10 detects a vehicle in an initial frame at S16. The method tracks the vehicle across subsequent frames, using its features, at S18.
The example system is used for vehicle-based speed enforcement. Therefore in a parallel process, the system applies camera calibration to convert a trajectory of the tracked vehicle from pixel-to-real coordinates to real-world coordinates at S20. The system computes a vehicle speed at S22 using distance (i.e., by comparing the real-world coordinates between frames) and time measurements (e.g., by relating the number of frames by the frame rate). The calculated speed is used to determine a violation at S24 for purposes of issuing a ticket.
Because conventional systems are often used for fee collection and traffic enforcement, the tracking data needs to yield accurate measurements. However, conventional tracking approaches can tend to yield erroneous measurements.
One approach for tracking vehicles, especially for the purpose of speed calculation, includes template matching, which focuses on searching a frame for same point(s) that were included in a previous frame. However, the effects of noise and camera disturbance on measurements can cause this approach to suffer from inaccuracy.
Another approach for tracking includes the mean-shift algorithm, which focuses on finding locations between frames having the same and/or similar (statistical) characteristics. While this approach is more robust against noise and camera disturbance, it tends to provide less accurate results because the determined locations may not be exact. This approach is also more computationally expensive.
Another approach for object tracking can include particle filtering. All of these conventional approaches can be well suited for providing tracking results when a function of the system does not require a calculation using the exact position of a tracked object. However, for systems that require accurate position information for computing output, such as for the purpose of vehicle speed-enforcement, an improved tracking method is desired.
A system and method are needed for improving the accuracy of tracking a feature location in frames and for computing the measurements in real-time. A system is desired which is more robust against camera projective distortion.